code: def test_create_embedder_using_toolkit(self):
      embedding_file = str(
          self.FIXTURES_ROOT / "embeddings/glove.6B.300d.sample.txt.gz"
      )
  
      class TinyTransformer(TokenEmbedder):
          def __init__(
              self, vocab, embedding_dim, hidden_size, intermediate_size
          ):
              super().__init__()
              self.embeddings = Embedding(
                  pretrained_file=embedding_file,
                  embedding_dim=embedding_dim,
                  projection_dim=hidden_size,
                  vocab=vocab,
              )
  
              self.transformer = TransformerStack(
                  num_hidden_layers=4,
                  hidden_size=hidden_size,
                  intermediate_size=intermediate_size,
              )
  
          def forward(self, token_ids: torch.LongTensor):
              x = self.embeddings(token_ids)
              x = self.transformer(x)
              return x
  
      tiny = TinyTransformer(
          self.vocab,
          embedding_dim=300,
          hidden_size=80,
          intermediate_size=40,
      )
      tiny.forward(torch.LongTensor([[0, 1, 2]]))
  
file_path: allennlp/tests/modules/transformer/toolkit_test.py
imports: import pytest
  import torch
  from torch.testing import assert_allclose
  from transformers import AutoModel
  from transformers.models.albert.modeling_albert import AlbertEmbeddings
  from allennlp.common import cached_transformers
  from allennlp.data.vocabulary import Vocabulary
  from allennlp.modules.token_embedders import Embedding, TokenEmbedder
  from allennlp.modules.transformer import TransformerStack, TransformerEmbeddings, TransformerPooler
  from allennlp.common.testing import AllenNlpTestCase
label_desc: Use image node_id 2 for calling the TestTransformerToolkit obj's underlying member method code with example usage: obj.test_create_embedder_using_toolkit() and returns: x